Transfer Learning

Transfer learning leverages knowledge gained from training a model on one task (the source) to improve its performance on a related but different task (the target), addressing data scarcity and reducing computational costs. Current research focuses on optimizing source data selection, employing various deep learning architectures like CNNs, LSTMs, and Transformers, and exploring techniques like data augmentation and hyperparameter optimization to enhance transferability across diverse domains. This approach significantly impacts various fields, from improving the accuracy and efficiency of medical image analysis and natural language processing to enabling more robust and adaptable AI systems in resource-constrained environments.